There are two reasons why neural swarming control must decouple each
agent's physical location from its internal self-localization. First, the
multiplicity of agents is a qualitative difference with brain circuits;
every hippocampal neuron in a biological network corresponds to the same
single agent (e.g., a rat) and has particular inputs from internal processing
or sensory inputs [1]} that contribute to the appearance
and location of the cell's place field. Given the analogy of agents to
neurons (Fig. REF A), an individual rat has many place fields
but an individual swarm agent should have only one. Further, the location of
an agent's place field cannot be identical to the agent's physical location,
which depends on the momentary vicissitudes of a entity operating in the
external world (or a simulation thereof). Second, experimental studies
have compellingly demonstrated that spatial path planning in hippocampal
networks may rely on activating sequences of place cells representing remote
locations [2]}, [3]}, [4]}, [5]}, indicating that
internal representations should be separable from an animal's (or agent's)
current physical location. Thus, NeuroSwarms assigns a distinct cue-based place
preference to each agent.
